The fungus Cryptococcus neoformans causes meningoencephalitis that kills more than 625,000 people each year. The goal of this research is to produce a mechanistic understanding of how the environmental changes experienced by this microbe upon entry into the human host stimulate the expression of virulence factors that advance fungal disease. There are major gaps in our understanding of how C. neoformans senses that it is in a host and coordinates a transcriptional response that leads to virulence factor expression. At the top of this chain of events, our knowledge of how individual characteristics of the host environment stimulate changes in transcription factor (TF) activity is patchy. At an intermediate level, we probably do not yet know all the TFs involved in the cryptococcal response to environmental change, nor how TF activities change during induction. Downstream of the TFs, we do not know which of their effects on gene expression are critical for the development of virulence factors. In this renewal application, we propose to use a powerful combination of molecular and computational biology to fill these gaps, focusing on the major cryptococcal virulence factor, its polysaccharide capsule. In Aim 1 we will exploit recent technical advances to assay strains deleted for all non-essential TFs for gene expression (by RNA-Seq) and capsule phenotypes (using a new automated assay and ELISAs). Incorporating this data set into our initial map of the TF network will greatly enhance it, informing our subsequent studies. In Aim 2 we propose to collect gene expression data on selected TFs at multiple time points during capsule induction. This will allow us to computationally model the network as a dynamic circuit that processes information from the environment and coordinates a complex transcriptional response. In Aim 3 we propose to identify the key changes in TF activity that transduce each capsule-inducing stimulus from the environment. We will computationally estimate these activity changes and then test, by genetic engineering, whether selected changes in TF activity are sufficient to substitute for a given environmental stimulus. In Aim 4, we will move downstream in the pathway and computationally generate hypotheses about which transcriptional changes in genes encoding non-regulatory proteins are necessary for capsule growth. We will then test each hypothesis by genetically eliminating the corresponding transcriptional change. This range of studies will be enabled by the synergistic efforts of two labs with complementary skill sets. This work will build on the foundation provided by our highly productive first grant period to elucidate fundamental mechanisms of transcriptional regulation, fill gaps in our understanding of the mechanisms of cryptococcal pathogenesis, and provide resources that will powerfully enable future research progress in areas ranging from fungal pathogenesis to computational genomics.